[HTML][HTML] Optimizing inventory control through a data-driven and model-independent framework
Abstract Machine learning has shown great potential in various domains, but its appearance
in inventory control optimization settings remains rather limited. We propose a novel …
in inventory control optimization settings remains rather limited. We propose a novel …
A deep q-network for the beer game: Deep reinforcement learning for inventory optimization
A Oroojlooyjadid, MR Nazari… - … & Service Operations …, 2022 - pubsonline.informs.org
Problem definition: The beer game is widely used in supply chain management classes to
demonstrate the bullwhip effect and the importance of supply chain coordination. The game …
demonstrate the bullwhip effect and the importance of supply chain coordination. The game …
Neuroevolution reinforcement learning for multi-echelon inventory optimization with delivery options and uncertain discount
ZU Rizqi, SY Chou - Engineering Applications of Artificial Intelligence, 2024 - Elsevier
The advanced information technology has enabled supply chain to make centralized optimal
decision, allowing to make a global optimal solution. However, dealing with uncertainty is …
decision, allowing to make a global optimal solution. However, dealing with uncertainty is …
Reinforcement learning provides a flexible approach for realistic supply chain safety stock optimisation
EE Kosasih, A Brintrup - IFAC-PapersOnLine, 2022 - Elsevier
Although safety stock optimisation has been studied for more than 60 years, most
companies still use simplistic means to calculate necessary safety stock levels, partly due to …
companies still use simplistic means to calculate necessary safety stock levels, partly due to …
Algorithmic approaches to inventory management optimization
An inventory management problem is addressed for a make-to-order supply chain that has
inventory holding and/or manufacturing locations at each node. The lead times between …
inventory holding and/or manufacturing locations at each node. The lead times between …
Control of dual-sourcing inventory systems using recurrent neural networks
A key challenge in inventory management is to identify policies that optimally replenish
inventory from multiple suppliers. To solve such optimization problems, inventory managers …
inventory from multiple suppliers. To solve such optimization problems, inventory managers …
A supply chain inventory management method for civil aircraft manufacturing based on multi-agent reinforcement learning
M Piao, D Zhang, H Lu, R Li - Applied Sciences, 2023 - mdpi.com
Effective supply chain inventory management is crucial for large-scale manufacturing
industries such as civil aircraft and automobile manufacturing to ensure efficient …
industries such as civil aircraft and automobile manufacturing to ensure efficient …
A practical end-to-end inventory management model with deep learning
We investigate a data-driven multiperiod inventory replenishment problem with uncertain
demand and vendor lead time (VLT) with accessibility to a large quantity of historical data …
demand and vendor lead time (VLT) with accessibility to a large quantity of historical data …
Multi-echelon supply chains with uncertain seasonal demands and lead times using deep reinforcement learning
We address the problem of production planning and distribution in multi-echelon supply
chains. We consider uncertain demands and lead times which makes the problem stochastic …
chains. We consider uncertain demands and lead times which makes the problem stochastic …
Simultaneous decision making for stochastic multi-echelon inventory optimization with deep neural networks as decision makers
M Pirhooshyaran, LV Snyder - arXiv preprint arXiv:2006.05608, 2020 - arxiv.org
We propose a framework that uses deep neural networks (DNN) to optimize inventory
decisions in complex multi-echelon supply chains. We first introduce pairwise modeling of …
decisions in complex multi-echelon supply chains. We first introduce pairwise modeling of …